The paper introduces a new dataset to assess the performance of machine
learning algorithms in the prediction of the seriousness of injury in a traffic
accident. The dataset is created by aggregating publicly available datasets
from the UK Department for Transport, which are drastically imbalanced with
missing attributes sometimes approaching 50\% of the overall data
dimensionality. The paper presents the data analysis pipeline starting from the
publicly available data of road traffic accidents and ending with predictors of
possible injuries and their degree of severity. It addresses the huge
incompleteness of public data with a MissForest model. The paper also
introduces two baseline approaches to create injury predictors: a supervised
artificial neural network and a reinforcement learning model. The dataset can
potentially stimulate diverse aspects of machine learning research on
imbalanced datasets and the two approaches can be used as baseline references
when researchers test more advanced learning algorithms in this area.

本文介绍了一种新的数据集，用于评估机器学习算法在交通事故中预测伤势严重性方面的表现。数据集通过聚合英国交通部的公开数据集来创建，这些数据集在数据维度上具有极度不平衡以及有时缺少高达 50％的属性，可以通过 MissForest 模型处理。本文还介绍了两种基准方法来创建伤害预测器：一种是监督的人工神经网络，另一种是强化学习模型。该数据集可以刺激机器学习研究在不平衡数据集方面的多个方面，并且这两种方法可以用作在这个领域测试更高级学习算法时的基准参考。